Automated image retrieval with image graph

ABSTRACT

An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/848,122 filed on Jun. 23, 2022, which is a continuation of U.S.application Ser. No. 16/592,006 filed on Oct. 3, 2019, which claims thebenefit of U.S. Provisional Application No. 62/770,159, filed Nov. 20,2018, each of which are incorporated by reference in their entirety.

BACKGROUND

This invention relates generally to retrieving images relevant to aquery image, and more particularly to identifying relevant images byexploring and traversing an image retrieval graph.

Identifying related images by computing systems is a challengingproblem. From a query image used as an input, image retrieval systemsattempt to identify relevant images that are similar or related to thequery image. Related images share features with one another, and forexample may be different images taken of the same scene, environment,object, etc. Though sharing such similarities, images may vary in manyways, even when taken of the same context. Lighting, viewing angle,background clutter, and other characteristics of image capture maychange the way that a given environment appears in an image, increasingthe challenge of identifying these related images. For example, fourdifferent images of the Eiffel Tower may all vary in angle, lighting,and other characteristics. Using one of these images to identify theother three in a repository of thousands or millions of other images ischallenging. As a result, the rate of successfully identifying relevantimages (and excluding unrelated images) in existing approaches continuesto need improvement. In addition, many approaches may also becomputationally intensive and require significant analysis when thequery image is received by the image retrieval system, which can preventthese approaches from effectively operating in live executingenvironments.

SUMMARY

An image retrieval system uses an image retrieval graph to identifyimages relevant to an image designated in a query. The image retrievalgraph represents images as image nodes that are connected by edges inthe graph. The edges have an edge weight that represents the similarityof two images. The image retrieval graph in one embodiment is generatedby identifying relevant images to connect in the graph and thenidentifying a weight for the edge connecting the relevant images in theimage retrieval graph. Each image may be represented by an imagedescriptor that characterizes the image as a vector. To determinerelevant images, the image descriptors between a given image and otherimages are compared to determine a similarity score between the imagesbased on the similarity between the descriptors. This similarity may bedetermined by an inner product between the image descriptors. Thesimilarity between different images may be determined, and the highlysimilar images may be selected for generating edges in the graph. In oneembodiment, this represents a k-NN selection: selecting the highest kimages that are nearest neighbors (as measured by the similarity score)to the selected number. After selecting which images to connect withedges, weights may be assigned to the edges based on the determinedsimilarity score, or may be determined based on a weight function, forexample a function that compares the images based on inlier counts. Byidentifying which image nodes to connect with edges weighing the edgesaccordingly, an image retrieval graph is generated that connects thevarious images in an image repository.

To search for relevant images in the image repository, the imageretrieval system receives a request identifying an image for which toidentify similar images in the image repository. To identify relevantimages, the image retrieval system searches the image retrieval graph.In performing the search, nodes encountered in the image retrieval graphare designated into a query result set (relevant results for the query),an exploration set (image nodes to be explored), and a traversal set(image nodes that are evaluated for relevance and addition to the queryresult set and the exploration set).

To perform the search according to one embodiment, the image retrievalsystem alternates between exploring nodes in the exploration set andtraversing (“exploiting”) nodes in the traversal set to add nodes to theexploration set (and the query result set) from the traversal set whenthe relevance of a node in the traversal set exceeds a threshold. Eachiteration alternating between these steps is termed an explore-exploititeration. The explore step identifies image nodes in the imageretrieval graph that may not be directly connected to the query imageand permits the search to reach image nodes distant from but relevant tothe query image. The traversal/exploit step thus uses the immediateneighborhood of each image node identified as relevant to the query andidentifies subsequent nodes to add to the query results and subsequentlyexplore.

Initially, the image node for the image associated with the query isadded to the exploration set. In performing an explore-exploititeration, the iteration evaluates the exploration set to identify nodesconnected to the nodes in the exploration set and updates the traversalset by adding the node to the traversal set. In the traversal set, thenodes are stored with the weight of the edges that connect the node inthe traversal set to the connected node. The highest weight associatedwith the node when explored is stored in the traversal set and termedthe traversal weight. When an image node in the exploration set isexplored (i.e., its connected nodes are added/updated in the traversalset), the node is removed from the exploration set. When the explorationset is empty, the traversal set is evaluated to add additional nodes tothe query result set and the exploration set. The traversal weights ofthe nodes are compared against a relevance threshold and when an imagenode's traversal weight is above the threshold, the image node is addedto the exploration set and the query result set. In some embodiments,the traversal set is organized as a heap prioritized by traversalweights. In these embodiments, the system can peek at the top of theheap and pop the image node off the heap when the traversal weight isabove the relevance threshold. When no nodes remain in the traversal setthat are above the relevance threshold, another iteration of theexplore-exploit iteration starts by exploring the newly-added imagenodes in the exploration set. In some embodiments, to ensure thatadditional query results continue to be identified, when the explorationset is empty and no image nodes in the traversal set are above thethreshold, one or more image nodes with the highest traversal weight isadded to the query result set and the exploration set. This may ensurethat there is always at least one node to explore. In this embodiment,the addition of the node(s) with the highest traversal weight, even ifnot above the threshold, may always be performed or continue until aminimum number of minimum explore-exploit iterations is performed.

The explore-exploit iterations may end when a stop condition occurs,such as when the traversal adds no additional nodes to the explorationset or when the number of query results exceeds a limit for queryresults.

By searching the image retrieval graph in this way, relevant images canbe identified with a limited execution time based on traversal of thegenerated image retrieval graph. For example, this may permit the imageretrieval system to perform a search without comparing or modifyingunderlying image descriptors to search an image retrieval graph or toevaluate similarity during runtime. When spatial verification is used torefine the edge weights, the exploration can also avoid topic driftduring the search by reducing false positives that might otherwisehappen when vector descriptors are compared between images. The searchalso thus identifies images that are still “close” to the query imagebecause the weights are validated by the spatial verification analysis,and query results may be ordered according to when the node wasencountered with sufficient relevance in the image retrieval graph.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example image retrieval system according to oneembodiment.

FIG. 2A shows an example process for generating edges between imagenodes of the image retrieval graph, according to one embodiment.

FIG. 2B shows example similarity scores and generated edges for an imageretrieval graph.

FIG. 3 shows a process for performing an image query on an imageretrieval graph, according to one embodiment.

FIGS. 4A-H shows an example query execution for an example imageretrieval graph and corresponding status of a query result set, anexploration set, and a traversal set.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION

FIG. 1 shows an example image retrieval system 100 according to oneembodiment. The image retrieval system identifies relevant images basedon an image associated with a received image query. The image query maybe received from another device or may be entered by a user in at theimage retrieval system 100. The image retrieval system 100 includesvarious modules and data stores for describing images and returningrelevant images in response to the query. These include a descriptorextraction module 110, a graph generation module 120, an image searchmodule 130, an image repository 140, and an image retrieval graph 150.The image retrieval system 100 may include additional computing modulesor data stores in various embodiments. Additional modules and devicesthat may be included in various embodiments are not shown in FIG. 1 toavoid undue complexity of description. For example, a user deviceproviding the image query and the network through which the devicecommunicates with the image retrieval system 100 are not shown in FIG. 1.

To prepare images for use with a query, relationships between relatedimages is characterized by nodes in an image retrieval graph 150 withweights between the image nodes describing similarity between theimages. To retrieve images related to an image query, the imageretrieval system 100 identifies an image node for the query image andexecutes a search on the image retrieval graph 150 based on the weightsbetween image nodes. In particular, the search alternates between 1)selecting relevant nodes to be added to the query results (termedtraversing or “exploiting”), and 2) exploring the relevance ofadditional nodes connected to the selected nodes. This permits thesearch to be executed with effective results in a comparatively shorttime that scales efficiently with the number of images considered in thesearch.

The image query received by the image retrieval system 100 is associatedwith or otherwise specifies an image. The image retrieval system 100identifies relevant images for the query image in the image repository140. The image repository 140 may include thousands or millions ofimages of various environments and objects that can be considered asrelevant results for a query. For example, the image repository 140 mayinclude images captured be vehicles during travel, or may be imagescaptured by individual users and uploaded to the image repository 140.Images may be considered related when they have the same person,landmark, scene, or otherwise share an object in view of the image. Aparticular image may be related to more than one other image. Someimages in the image repository 140 may be labeled with a relationship toother images, such as identifying that two images each show the sameobject. These images may be used as a training set, for example, totrain a descriptor model as discussed with respect to the descriptorextraction module 110. Images are typically represented in various colorspaces, such as red-blue-green (RGB) or hue-saturation-value (HSV). Theimages in the image repository may significantly vary in appearance fromone another, even for images which are related. The various images mayvary significantly with respect to brightness, color, field of view,angle of view, additional objects or environment captured in the image,and so forth.

The descriptor extraction module 110 extracts image descriptors from theimages of the image repository. The image descriptors are a computeddescription of the images that may be used to compare images in theimage retrieval system 100. The image descriptors may include a vectorrepresentation of the image, which provides a compact representationthat may be compared across images to determine a similarity betweenimages as a whole. In one embodiment the vector representation is avector of 2048 values. The image descriptors may also include featuredescriptors that describe ‘interesting’ portions of the image, forexample based on detected edges or other characteristics of the image.The feature descriptors may be used to identify matching portions of twoimages. Thus, while the vector representation may be a general, globalcharacterization of the image, the feature descriptors may describeindividual features, points, or sections of the image that can becompared to determine whether images match.

To generate the vector representation of an image, the descriptorextraction module 110 may apply a neural network trained to receive animage and output a vector representation of that image. The imageretrieval system 100 may use a pre-trained neural network that outputs avector representation of an image, or may train an image based on thetraining set of images in the image repository 140 having labeledrelationships. A similarity score between images is used to train theneural network as an objective of the network, such that the similarityscore is compared with the labeled relationships between images and usedto train the network based on an error between predicted similarity andlabeled relationship. In one embodiment, the neural network is aCNN-based R-MAC descriptor model fine-tuned for landmark retrieval. Inother embodiments, the vector representation may be generated based on amodel using local invariant features or a bag-of-words model.

The feature descriptors may be specific to a particular location withinthe image, such that different images taken of a given object generatefeature descriptors that match across each image. In one embodiment thefeature descriptors are generated based on a Scale Invariant FeatureTransform (SIFT). In another embodiment, the feature descriptors aregenerated based on a deep local features model (DELF), which may useconvolutional neural networks to generate features describing ofportions of the image. In one embodiment, the deep local features modelextracts feature vectors from an image, and the dimensionality of thevectors may be reduced based on principle component analysis (PCA).

The graph generation module 120 uses the image descriptors to generatethe image retrieval graph. The graph generation module 120 evaluates theimage descriptors to identify similar images and generate edge weightsbetween the image nodes.

FIG. 2A shows an example process for generating edges between imagenodes of the image retrieval graph, according to one embodiment. FIG. 2Bshows example similarity scores and generated edges for an imageretrieval graph. This process may be performed by the descriptorextraction module 110 and the graph generation module 120. Initially,image descriptors are extracted 200 from each image in the imagerepository, which may be performed by the descriptor extraction module110 as discussed above. In some examples, the image descriptors for animage may be pre-generated or may already be associated with the imagesin the image repository. Next, the graph generation module 120 evaluateseach image to identify which other images to connect with the image inthe graph and what weight to assign to the edge in the graph. Toidentify images to connect in the graph, the graph generation module 120evaluates similarity 210 by determining a similarity score between thesubject image and other images in the image repository. In oneembodiment, the similarity score is an inner product between the vectorrepresentation of the subject image and the vector representation of theother image. The inner product may be a dot product or may be anothervector similarity function for comparing similarity between vectors. Asa working example, a similarity score table 250 shows example similarityscores calculated for image A with respect to other images, includingimages B, C, D.

Next, the process selects 220 images to connect to the subject imagebased on the similarity scores. The similarity scores are ranked, andthe top k images are selected from the ranked list. The number ofselected images, k, may vary in different implementations, and may be50, 100, 200, or more. In this embodiment, the selected images representthe nearest neighbors (k-NN) to the subject image, as measured by thesimilarity scores. In another embodiment, images above a thresholdsimilarity score are selected to be connected in the image retrievalgraph, which may include more or fewer than k. In another embodiment,both approaches may be used, to connect at least the top k nearestneighbors and additionally any images over the threshold score. In thisexample, this may ensure at least k image nodes are connected, whilealso allowing additional connections when more images are highlysimilar. As a result, the image retrieval graph may be sparse, includingk-NN connections for each image node. In a repository of 1M images witha value of 100 for k, for example, each image node is connected to 100other nodes in the image retrieval graph.

Next, a weight may be determined 230 for each pair of images to beconnected. In one embodiment, the weight is the similarity scorediscussed above. In other embodiments, the weight may be determinedbased on a comparison of the feature descriptors of the images. Inparticular, the feature descriptors may be compared to identify featuresthat can be matched between two images. This analysis may primarily orexclusively evaluate inliers between two images, indicating featuresthat match between two images, even when additional features do notmatch. As one example, the comparison may be based on a Random SampleConsensus (“RANSAC”) algorithm. This algorithm may apply various imagetransforms to attempt to identify feature matches across images andselect a transform that permits a maximum number of features to match.The number of inliers when comparing the feature descriptors of theimages may be used as the weight between images in the image retrievalgraph. By using analysis based on inliers to weight edges between imagenodes, the edges between nodes may thus verify actual similarity betweenimages based on descriptions of portions of the images at a finer scalethan the global description that may be determined from the similarityscore determined from vector representations.

After determining weights between image nodes to be connected, an edgebetween the image nodes in the graph may be generated 240 with thedetermined weight. In one embodiment, the connections and weightsbetween image nodes may be represented as a sparse matrix, where a valueof zero (or no value) at an intersection of two image nodes representsno edge between the image nodes, and a nonzero value is the weight ofthe edge between the nodes at the position.

A partial image retrieval graph 260 shows the connection between imagenodes representing image A and the corresponding weights in oneembodiment. In this example, three image nodes are selected for image A,for example if k is 3 in a k-NN approach to connected node selection.After weighting the nodes, weights may be generated as shown in thepartial image retrieval graph 260. In this example, while the nodes wereselected based on the similarity scores as shown in the examplesimilarity score table 250, the nodes are assigned weights that aregenerated based on identified inliers between the images. In thisexample, the inlier score between images A and C is 87, between A and Bis 80, and between A and D is 15. This example illustrates that althoughimages A and D have a similarity score that was high enough to includean edge between A and D, the verification based on comparison of featuredescriptors yielded a relatively low inlier score between A and D. Thisprocess may be repeated for each of the images to be added to the imageretrieval graph 150 to select 220 nodes and generate 240 edges betweenthem.

FIG. 3 shows a process for performing an image query on an imageretrieval graph, according to one embodiment. This process may beperformed by the image search module 130 with respect to the imageretrieval graph 150. To begin the process, the image search module 130receives 300 a search query indicating an image for which to identifyrelated images. The query may include the image, or may specify an imageat another location, such as an image in the image repository 140. Next,the image search module 130 identifies whether the image has anassociated image node in the image retrieval graph. When the image doesnot have an associated image node, the image is added 310 to the imageretrieval graph. As discussed with respect to FIG. 2 , image descriptorsmay be extracted from the image, similar images may be selected forwhich to generate an edge, and edges generated with weights based on thesimilarity between the images. Accordingly, when an image search isrequested for a new image, the new image can be quickly added to theimage retrieval graph without requiring modification to the image nodesor edges related to other images. In addition, subsequent requests withthat image may be processed quickly because the image may already beincluded in the image retrieval graph.

To perform the search, the image search module 130 uses sets of imagenodes to organize the search. These sets of image nodes may include aquery result set, an exploration set, and a traversal set. Initially,each of these sets may be initialized as empty. To begin the search, thequery image node associated with the query image is added 320 to theexploration set. During execution of the search, the image search module130 may use explore-exploit iterations to alternate between exploringedges from image nodes of interest (“exploring”), and evaluating edgesto identify additional nodes of interest (“exploiting”).

The query result set stores the set of image nodes for images to bereturned as relevant results of the query. The query result set maystore the query results as an ordered list or queue, such thatadditional items added to the query result set are added to the tail ofthe query result set and the items in the query result set are alsotypically ordered according to expected relevance. In embodiments inwhich the image graph is traversed according to relevance, the relevanceorder is a result of the traversal order and may require no furtheranalysis to determine a result order.

During the explore phase, the edges connected to nodes in theexploration set are explored to identify the relevance of other nodesbased on the connection to a node of interest in the exploration set. Asthe edges for each image node in the exploration set are evaluated, theimage nodes connected to an image node in the exploration set areupdated 330 in the traversal set. The traversal set stores a set ofimage nodes that are connected to explored nodes and includes atraversal weight associated with the image node. The traversal weight inthe traversal set may specify the highest edge weight encountered by thequery as nodes are explored. In this way, the traversal weightrepresents a relevance of a given image node based on the nodes that areof interest in the current query exploration. In one embodiment imagenodes are popped from the exploration set and the edges connected tothat node are updated in the traversal set.

Accordingly, to update the traversal set, an image node that has notpreviously been explored (e.g., is not in the traversal set), may beadded to the traversal set with a traversal weight of the edge weightconnecting the explored image node to the node of interest. For imagenodes that have previously been explored (e.g., is in the traversalset), the traversal weight may be updated with the edge weight connectedto the currently-explored image node. When the edge weight is higherthan the traversal weight, the traversal weight may be set to the edgeweight, and when the edge weight is lower, the traversal weight may beunchanged. When an image node is already in the query result set, thetraversal set is not updated, since that image node was alreadyexplored.

In some embodiments, the traversal set is organized as a heap thatprioritizes image nodes according to the traversal weight, such that thetop item in the heap is the image node with the highest traversalweight. The traversal set may also be stored or ordered in structuressuch that the highest traversal weights may be readily identified.

During the traversal or “exploit” phase, the image nodes in thetraversal set are evaluated to determine which are sufficiently relevantto be added to the query result set and to the traversal set. At leastone image node in the traversal set is added 340 to the query set andthe exploration set based on the traversal weights of the image nodes.

As one example, the traversal weight is compared to a relevancethreshold and images nodes above that threshold are considered relevantto the search query and added 340 to the query result set and theexploration set. In embodiments in which the traversal set is a heap,the traversal phase peeks at the top traversal value of the traversalset, and when the traversal value exceeds the relevance value, the imagenode is popped off the traversal set and added to the query result setand exploration set. The next image node is peeked at until the toptraversal value is not above the relevance threshold. Because the imagenodes may be evaluated in the traversal set according to the traversalweights, the addition of the image nodes to the query result set is inan expected relevance order. In one embodiment, when the traversal nodeof an image node does not exceed the relevance threshold, the image nodemay be kept in the traversal set, such that each image node evaluated inthe query traversal is present in either the query result set or thetraversal set. In another embodiment, when the traversal set has notraversal weights over the relevance threshold, the traversal set may beemptied at the end of the traversal phase.

In one embodiment, when no nodes in the traversal set have a traversalweight over the relevance threshold, at least one node is added to theexploration set from the traversal set to ensure that the querycontinues to identify query results. Stated another way, in thisembodiment the traversal phase adds at least a minimum number of imagenodes in the traversal set to the query result set and the explorationset and also includes additional nodes in the traversal set that have atraversal weight above the relevance threshold. The inclusion of thesenodes below the relevance threshold may be performed for eachexplore-exploit iteration, and in some embodiments is performed for alimited number of explore-exploit iterations or until a minimum numberof image nodes are in the query result set.

Using the alternating explore-exploit iteration, the search thusevaluates nodes closer to the query image node and explores the graphwhen edge weights are high enough to meet a relevance threshold. As aresult, the query can be executed without modifying the image retrievalgraph (except, if necessary, to add 310 the query image) and repeatqueries can be executed without requiring run-time comparison of imagedescriptors. Further, image nodes that initially may not appear relevantto the initial query (e.g., by having no edge or an edge weight belowthe relevance threshold) may become relevant as other image nodes areexplored that do have a sufficiently high edge weight to the image node(thus putting the traversal weight of the image node over the relevancethreshold). Similarly, when the edge weights are based on an inliercount, the relevance threshold can reduce false positives and ensuresufficient relationship between the images. This is illustrated forimage nodes D and E in the example discussed below. In additionalembodiments, the relevance score may be increased based on the number ofexplore-exploit iterations, such that as the query explores further awayfrom the query image, the threshold for continuing the query increases.

The explore-exploit iterations may be repeated until a stop condition isreached, such as a number of query result reaching a maximum, or untilthe exploration set is empty after a traversal phase (e.g., no nodes inthe traversal set exceed the relevance threshold and none were added tothe exploration set).

FIGS. 4A-H shows an example query execution for an example imageretrieval graph 400A-H and corresponding status of a query result set410A-H, an exploration set 420A-H, and a traversal set 430A-H. In thisexample, the threshold for relevance is a traversal weight of 60. Inthis example, the query image corresponds to image node A and FIG. 4Aillustrates the addition of the query image node to the exploration set420A. In the image retrieval graph 400A-400H, nodes which are includedin the exploration set have a dotted border, nodes which have beenexplored are filled with a diagonal pattern, and edge connections in thetraversal set are illustrated with dashed lines.

Between FIGS. 4A and 4B, an explore phase is performed to explore edgesfor the exploration set 420A, which includes image node A. The edgesconnected to image node A are used to update the traversal set, suchthat the connection from node A to node C having a weight of 87, theconnection from node A to node B having a weight of 80, and theconnection from node A to node D having a weight of 15 are added to thetraversal set 430B as shown in FIG. 4B. Next, a traversal phase isperformed between FIGS. 4B and 4C. During this phase, the traversal set430B is evaluated with respect to the relevance threshold. Initially,image node C is evaluated with respect to its traversal weight of 87.This value is higher than the relevance threshold of 60, so image node Cis removed from the traversal set and added to the query result set 410Cand exploration set 420C. Likewise, image node B has a traversal weightof 80, which is higher than the relevance threshold of 60, so image nodeB is removed from the traversal set and added to the query result set410C and exploration set 420C. When the traversal weight of image node Dis evaluated, it has a traversal weight of 15, which is lower than thethreshold relevance, so image node D remains in the traversal set 430B.Since the traversal set now has no image nodes with a traversal weightabove the relevance threshold, the traversal phase ends and the processmay evaluate whether to perform another explore-exploit iteration. Inthis case, there are image nodes in the exploration set 230C remainingto be explored, so another explore-exploit iteration is performed.

From FIG. 4C to 4D, another exploration phase is performed, which nowexplores from the nodes identified as relevant from the prior exploitphase. In this exploration phase, the image node C is explored andremoved from the exploration set 420C followed by image node B until theexploration set is empty as shown in exploration set 420D. In exploringimage node C, the traversal set 430C is updated with the edge from imagenode C to image node F with a traversal weight of 40, and the edge fromimage node C to image node D with a traversal weight of 66. Image node Cand image node B also have an edge connecting to image node A, which isnot added to the traversal set because it is the query image node. Inexploring image node B, the traversal set 430C is updated with the edgefrom image node B to image node E with a traversal weight of 55, and theedge from image node B to image node D with a traversal weight of 107.In updating with the edge from image node B to image node D, thetraversal weight may update the traversal weight for image node D thatwas determined from the edge from image node C to image node D (i.e.,that had a value of 66). When the exploration set 420C is empty, theprocess proceeds to a traversal phase for FIG. 4D to examine thetraversal set 430D and progress to FIG. 4E. In traversing the traversalset 430D, the traversal weight for image node D is 107, which is abovethe relevance threshold of 60, and image node D is thus added to thequery result set 410E and exploration set 420E and removed from thetraversal set 430E. The remaining image nodes E and F have traversalweights below the relevance threshold, so the traversal phase ends. Theprocess evaluates whether to perform an additional explore-exploititeration and based on the image node (image node D) now in theexploration set, another iteration is performed as shown in FIGS. 4E-4F.

From FIG. 4E to 4F, an exploration phase is performed to explore theimage node in the exploration set 420E. In this phase, the image node Din the exploration set 420E is evaluated, which has edges to image nodesA, B, C, E, and G. Image node A is the query image node, and image nodesB and C are already in the query result set (and previously explored) sothese nodes are not updated in the traversal set 420E. The edge fromimage node D to image node E has an edge weight of 65, and which isupdated in the traversal set 430F to replace the prior traversal weightof 55. To update with the edge weight from image node D to image node G,image node G is added to the traversal set 430F with a traversal weightof 9. After evaluating the connections to image node D, the image node Dis removed from the exploration set 420E and the exploration set isempty, allowing the process to continue to a traversal phase for FIG.4F. In traversing the traversal set 430F, the traversal weight for imagenode E now exceeds the relevance threshold (unlike in FIG. 4D), andimage node E may be added to the query result set 410G and theexploration set 420G and removed from the traversal set 430G. Theprocess determines to perform an additional explore-exploit iterationbecause the exploration set 420G is not empty. From FIG. 4G to FIG. 4H,the explore phase evaluates image node E in the exploration set 420G.Edges from image node E to image nodes B and D are not updated to thetraversal set 430G because those image nodes were previously exploredand are included in the query result set 410G. The edge from image nodeE to image node H is updated in the traversal set 430H and with atraversal weight of 52. After exploring image node E, the explorationset 420H is empty, and the exploration phase ends. In one embodiment, atthe traversal phase, no image nodes in the traversal set exceeds therelevance threshold of 60. In this embodiment, at the end of thisexplore-exploit iteration, there are no image nodes in the explorationset 420H, indicating that there are no further image nodes to considerof sufficient relevance in the graph.

In other embodiments (not shown) in which the traversal phase includesat least one image node from the traversal set (regardless of whetherthe highest traversal weight exceeds the relevance threshold), imagenode H may be added to the exploration set and the query result set ashaving the highest traversal weight in the traversal set 430H. In thisembodiment, additional explore-exploit iterations explore image node Hand may be followed by image node G and finally image node F.

Finally, the images associated with the image nodes in the result queryset 410 are returned as the query result set of images for the imageassociated with image node A.

As shown by this example, the explore-exploit iterations permitseffective exploration of the image retrieval graph 400A-H and preservesa relevance order in the query result set based on the order in whichnodes were encountered as they became sufficiently relevant in thetraversal set. In addition, because the nodes can be traversed based onthe weights, additional analysis is not required to evaluate theindividual images at runtime of the search query. As a result, thisapproach provides an effective way to perform an image query with a lowruntime (particularly for images that already exist in the imageretrieval graph) and effectively identifies additional images in therepository without significant deviation from the character of the queryimage.

The foregoing description of the embodiments of the disclosure has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the disclosure to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of thedisclosure in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the disclosure may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the disclosure may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the disclosure is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

1-20. (canceled)
 21. A computer system for automated image retrieval,the computer system comprising: a processor configured to executeinstructions; a non-transient computer-readable medium comprisinginstructions executable by the processor for: determining similarityscores for a query image for each other image of a set of other imagesin the set of images, the similarity scores based on similarity of animage descriptor of the query image and an image descriptor of the otherimage; connecting, in an image retrieval graph, a set of image nodes,which correspond to a subset of other images selected based on thesimilarity scores, to a query node corresponding to the query image withconnections having an edge weight based on inliers between the connectednodes; and identifying relevant images to the query image based ontraversal of the image retrieval graph from the query node based on edgeweights between nodes in the image retrieval graph.
 22. The computersystem of claim 21, wherein identifying relevant images to the queryimage based on traversal of the image retrieval graph comprisestraversal of the image retrieval graph with an explore-exploitalgorithm.
 23. The computer system of claim 21, wherein the subset ofother images are selected based on a ranked number of a k-nearestneighbor (k-NN) algorithm applied to the similarity scores.
 24. Thecomputer system of claim 21, wherein the instructions are furtherexecutable for determining the edge weight for a connection between thequery node and another node based on an inlier score of a Random SampleConsensus (RANSAC) algorithm applied to the query image with respect tothe other image associated with the other node.
 25. The computer systemof claim 21, wherein the image descriptor of the query image comprises aglobal description of the query image.
 26. The computer system of claim21, wherein the instructions are further executable for determining theimage descriptor of the query image by applying the query image to acomputer model that outputs a compact vector representation of the queryimage.
 27. The computer system of claim 21, wherein the image descriptorincludes one or more feature descriptors describing portions of theimage.
 28. The computer system of claim 21, wherein determining thesimilarity scores comprises applying a vector similarity function to theimage descriptor of the query image and the image descriptor of theother image.
 29. A method for automated image retrieval, the methodcomprising: determining similarity scores for a query image for eachother image of a set of other images in the set of images, thesimilarity scores based on similarity of an image descriptor of thequery image and an image descriptor of the other image; connecting, inan image retrieval graph, a set of image nodes, which correspond to asubset of other images selected based on the similarity scores, to aquery node corresponding to the query image with connections having anedge weight based on inliers between the connected nodes; andidentifying relevant images to the query image based on traversal of theimage retrieval graph from the query node based on edge weights betweennodes in the image retrieval graph.
 30. The method of claim 29, whereinidentifying relevant images to the query image based on traversal of theimage retrieval graph comprises traversal of the image retrieval graphwith an explore-exploit algorithm.
 31. The method of claim 29, whereinthe subset of other images are selected based on a ranked number of ak-nearest neighbor (k-NN) algorithm applied to the similarity scores.32. The method of claim 29, further comprising determining the edgeweight for a connection between the query node and another node based onan inlier score of a Random Sample Consensus (RANSAC) algorithm appliedto the query image with respect to the other image associated with theother node.
 33. The method of claim 29, wherein the image descriptor ofthe query image comprises a global description of the query image. 34.The method of claim 29, further comprising determining the imagedescriptor of the query image by applying the query image to a computermodel that outputs a compact vector representation of the query image.35. The method of claim 29, wherein the image descriptor includes one ormore feature descriptors describing portions of the image.
 36. Themethod of claim 29, wherein determining the similarity scores comprisesapplying a vector similarity function to the image descriptor of thequery image and the image descriptor of the other image.
 37. Anon-transitory computer-readable medium containing computer program codeexecutable by a processor for the processor for: determining similarityscores for a query image for each other image of a set of other imagesin the set of images, the similarity scores based on similarity of animage descriptor of the query image and an image descriptor of the otherimage; connecting, in an image retrieval graph, a set of image nodes,which correspond to a subset of other images selected based on thesimilarity scores, to a query node corresponding to the query image withconnections having an edge weight based on inliers between the connectednodes; and identifying relevant images to the query image based ontraversal of the image retrieval graph from the query node based on edgeweights between nodes in the image retrieval graph.
 38. Thenon-transitory computer-readable medium of claim 37, wherein identifyingrelevant images to the query image based on traversal of the imageretrieval graph comprises traversal of the image retrieval graph with anexplore-exploit algorithm.
 39. The non-transitory computer-readablemedium of claim 37, wherein the subset of other images are selectedbased on a ranked number of a k-nearest neighbor (k-NN) algorithmapplied to the similarity scores.
 40. The non-transitorycomputer-readable medium of claim 37, wherein the computer program codeis further executable for determining the edge weight for a connectionbetween the query node and another node based on an inlier score of aRandom Sample Consensus (RANSAC) algorithm applied to the query imagewith respect to the other image associated with the other node.